
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12331-6
10.1016/j.heliyon.2024.e36300
e36300
Research Article
Precision mapping of snail habitat in lake and marshland areas: Integrating environmental and textural indicators using Random Forest modeling
Zhang Xuedong ab
Lv Zelan a
Dai Jianjun c
Ke Yongwen c
Chen Xinyue def
Hu Yi huyi@fudan.edu.cn
def⁎
a School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, 102627, China
b Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, 100038, China
c Schistosomiasis Station of Prevention and Control in Guichi District 247100, Anhui Province, China
d Department of Epidemiology, School of Public Health, Fudan University, Shanghai, 200032, China
e Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, 200032, China
f Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, 200032, China
⁎ Corresponding author. huyi@fudan.edu.cn
13 8 2024
30 8 2024
13 8 2024
10 16 e3630010 5 2024
7 8 2024
13 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Schistosomiasis japonica continues to pose a significant public health challenge in China, primarily due to the widespread distribution of Oncomelania hupensis, the sole intermediate host of Schistosoma. This study aims to address the constraints of existing remote sensing analyses for identifying snail habitats, which frequently neglect spatial scale and seasonal variations. To this end, we adopt a multi-source data-driven Random Forest approach that integrates bottomland and ground-surface texture data with traditional environmental variables, enhancing the accuracy of snail habitat assessments. We developed four distinct models for the lake and marshland areas of Guichi, China: a baseline model incorporating ground-surface texture, bottomland variables, and environmental variables; Model 1 with only environmental variables; Model 2 adding ground-surface texture and environmental variables; and Model 3 integrating bottomland with environmental variables. The baseline model outperformed the others, achieving a true skill statistic of 0.93, an accuracy of 0.97, a kappa statistic of 0.94, and an area under the curve of 0.99. Our analysis pinpointed critical high-risk snail habitats distributed in a belt-like pattern along major water bodies, near the Yangtze River, QiuPu River, and around Shengjin Lake, Jiuhua River, and Qingtong River. These insights can aid local health authorities in more efficiently allocating limited resources, developing effective snail surveillance and control strategies to combat schistosomiasis. Additionally, this approach can be adapted to localize other endemic hosts with similar ecological characteristics.

Keywords

Multi-source data
Schistosomiasis
Potential risk area
Machine learning
Recognition
==== Body
pmc1 Introduction

Schistosomiasis is a severe parasitic disease with a significant effect on human health. It is highly prevalent in subtropical and tropical regions of Asia, Africa, South America, and the Middle East [1]. The primary parasitic species responsible for human infection are Schistosoma japonicum, Schistosoma mansoni, and Schistosoma aegypti. Among these, schistosomiasis japonica is predominantly found in Southeast Asia and South China [2]. In China, schistosomiasis japonica remains a major public health challenge because of its complex etiology and multiple transmission pathways [3]. The transmission of schistosomiasis japonica is closely related to the distribution of its only intermediate host, Oncomelania hupensis (snail), which governs the spread of schistosomiasis [4]. Consequently, accurate identification and subsequent monitoring of snail habitats has become essential for controlling and mitigating schistosomiasis.

The traditional approach to snail inspection is both labor-intensive and time-consuming, often yielding suboptimal results [5]. Snail reproduction is intricately linked to climatic conditions, environmental attributes, and socioeconomic factors. Therefore, numerous studies have employed remote sensing technology to identify snail habitats. For instance, Walz et al. extracted seven environmental factors from remote sensing images, using a habitat suitability index to identify snail habitats [6]. Rao et al. explored the relationship between snail habitats and environmental factors through correlation analysis and curve regression analysis to predict snail breeding sites [7]. Similarly, Xue et al. extracted three important environmental factors and established a multiple regression analysis model to predict snail distribution [8]. Further advancing this field, Zhang et al. constructed 12 different models, including statistical and machine learning methods, to predict two types of potential snail habitats in Anhui province, among which the Random Forest (RF) model performed outstandingly [9]. Collectively, these studies converge on a prevalent methodology for snail detection, involving the extraction of environmental variables from remote sensing images and the application of models to assimilate and interpret the environmental characteristics associated with snail habitats. This approach facilitates the identification and mitigation of snail habitat risks.

Previous studies primarily focused on natural and socio-environmental factors, often overlooking the importance of ground-surface texture information in snail habitat analysis. Remote sensing images capture various textural attributes reflecting the spatial distribution structure of ground objects [10]. These attributes are invaluable for discerning the surface characteristics of distinct features [11]. Snail habitats often exhibit unique land surface features identifiable through these textural attributes. Additionally, past studies neglected a key variable closely related to snail habitats – the distribution of bottomland areas characterized by "winter land-summer water". This refers to water levels rising from May to October (wet season), submerging the whole substratum where snails reside, rendering the area unsuitable for snail activity. This bottomland area has shown a close correlation with the distribution of snails; however, information on bottomland distribution has been used only to test whether an area is suitable for snail habitation and has not been introduced as a continuous variable in the model [12,13]. Therefore, this study hypothesized that harnessing texture information and quantifying the distance from snail habitats to these bottomlands can significantly improve precision in identifying snail habitats.

This study aimed to identify snail habitats distributed in Guichi, China, a typical schistosomiasis-endemic region along the Yangtze River. To achieve this objective, two new variables were incorporated into our analysis: (i) texture information of the ground surface and (ii) data on bottomland distribution, alongside traditional environmental variables. Using this enriched dataset, a model specifically designed for snail habitat identification was constructed using the RF algorithm. This model primarily facilitates the probabilistic delineation of snail habitats and helps generate a habitat probability distribution map.

2 Data and methodology

2.1 Study area

Located in the lower-middle section of the Yangtze River Basin in Guichi is a focal area for schistosomiasis japonica research in China. Covering an area of 2516 km2, its geographic features are notable for numerous rivers such as Qiupu, Jiuhua, Qingtong, and Baiyang in the north. By contrast, the central and southern areas of the region are predominantly hilly. The combination of a favorable climate, plentiful water resources, and lush vegetation fosters an environment conducive to snail reproduction, a key factor in schistosomiasis transmission [14]. This study primarily focuses on the lake and marshland areas in the Guichi district, as shown in Fig. 1.Fig. 1 Study area location in the lake and marshland areas of Guichi, Chizhou, Anhui Province, China. The study area is located in the northern part of Guichi, near the south bank of the middle and lower reaches of the Yangtze River.

Fig. 1

2.2 Data source

2.2.1 Snail data

Snail data were obtained from field surveys conducted throughout Guichi between March and May 2021, which included geographic information (latitude and longitude coordinates) on snail presence, snail population types, and environmental characteristics of their habitats. A total of 108 survey sites covering snail populations in lakes and marshlands were identified. An equal number of 108 snail-absent locations were selected as the control group based on criteria such as areas without snails, including rooftops, roads, construction sites, and permanent water bodies. A total of 216 sample sites were included in the study area.

2.2.2 Covariate data

2.2.2.1 Environmental data

Covariates previously identified as significant in determining snail habitat distribution were chosen [5]. The environmental data includes precipitation (PRE), distance to bottomland (DB), distance to water bodies (DW), land surface temperature (LST), normalized difference vegetation index (NDVI), wetness (WET), land use (LU), and nighttime light (NL). Table 1 details the sources and resolution of all environmental data.Table 1 Summary of the environment variables used in this study.

Table 1Covariate	Spatial Resolution	Source	Reference	
PRE	30m	China Meteorological Data Service Centre	https://data.cma.cn [15]	
DB	/	SDWI	Jia et al., 2019 [16]	
DW	/	Open Street Map	https://www.openstreetmap.org[17]	
LST	30m	Mono-window algorithm	Rouse et al., 1974 [18]	
 NDVI	30m	Band math	Qin et al., 2003 [19]	
WET	30m	Optical image inversion	Huang et al., 2002 [20]	
LU	10m	EULUC-China	Gong et al., 2020 [21]	
NL	500m	NPP-VIIRS	Chen et al., 2021 [22]	
Note: PRE, precipitation; DB, distance to bottomland; DW, distance to waterbody; LST, land surface temperature; NDVI, normalized difference vegetation index; WET, wetness; LU, land use; NL, nighttime light.

Natural environmental factors in the study area were derived from Landsat 8 images. NDVI was used to assess vegetation cover in the study area [18], the Mono-window algorithm to estimate land surface temperature [19], and the Kirchhoff Transform (K-T) to gauge wetness [20]. The moisture content derived from the K-T provides insights into soil and vegetation moisture. We computed the DW variables using spatial analysis methods in ArcGIS 10.2 (ESRI Inc., Redlands, CA, USA).

Previous studies explored the relationship between snail habitats and waterbody distribution [9]. However, they often overlooked the effect of seasonal changes on water distribution, which is critical for the reproduction of snails. The present study addressed this gap by focusing on bottomland distribution, which reflects these seasonal variations more accurately. For identifying the bottomland area, Sentinel-1 radar images, sensitive to waterbody, were assessed, and the Sentinel-1 dual-polarized water index (SDWI) algorithm proposed by Jia et al. was used to segment the water and land portions of the study area [16]. The waterbody during the abundant water period (May to October) and the dry water period (November to April) were segmented, and the difference between these periods was calculated to determine seasonal water level changes. This identified bottomland areas characterized by "winter land-summer water". The nearest distance from each sampling point to the bottomland was calculated using the Coverage tool in ArcGIS10.2, deriving the variable "DB."

2.2.2.2 Texture data

Texture information reflects the spatial information of ground objects, and it is essential for identifying features of ground objects. Therefore, we introduced the texture information to characterize the spatial distribution of snail habitats. Eight texture feature indicators were used: mean (B1), variance (B2), standard deviation (B3), contrast (B4), dissimilarity (B5), entropy (B6), angular second moment (B7), and correlation (B8); Among these indicators, the mean indicator reflects the average brightness relationship between pixels and their neighboring pixels in the image. Variance illustrates the extent of grayscale variation in localized regions of the image. Standard deviation assesses the concentration level of pixel values within the matrix. Contrast reveals the degree of variation in grayscale among pixels within local regions. Dissimilarity signifies the linear correlation of contrast among local pixels. Entropy quantifies the richness of texture information within the image, with higher entropy values indicating more texturally complex images. The angular second moment reflects the uniformity of gray scale distribution and texture coarseness of the image. Correlation used to measure how similar the gray levels of an image are in the row or column direction, the larger the value, the greater the correlation. These were extracted from Landsat 8 images using the Gray-Level Co-occurrence Matrix (GLCM), a commonly used texture analysis method particularly suitable for optical image texture features [23]. The eight indicators were computed in ENVI 5.3 software (Exelis inc., Boulder, CO, USA). Table 2 shows the texture information categories and their labels.Table 2 Types of texture indicators used in the study.

Table 2Acronyms	Textural indicator	Meaning of the indicator	
B1	Mean	Degree of regularity of texture	
B2	Variance	Deviation of the image element value from the mean value	
B3	Standard Deviation	Deviation of the image element value from the mean value	
B4	Contrast	Local gray level uniformity of an image	
B5	Dissimilarity	Local gray level uniformity of an image	
B6	Entropy	Amount of information an image has	
B7	Angular Second Moment	Uniformity of the gray level distribution of an image	
B8	Correlation	Degree of similarity between elements	

2.3 Methodology

Many studies have demonstrated that the efficacy of the RF model in predicting potential snail habitats [9,24,25]. We conducted a comparison between the RF model and other machine learning models, with the results presented in Appendix Table S1. The RF model, which exhibited the best performance, was subsequently employed to identify snail habitats. The detailed process is illustrated in Fig. 2.Fig. 2 Technical workflow of snail habitat identification study. D: input data, I: model indicators, B: model building, V: validation indicators.

Fig. 2

In the specific processing process, the following research hypothesis was tested: incorporating texture information from Landsat 8 images and quantifying the proximity of snail habitats to bottomland improves snail habitat identification accuracy. The baseline model, Model 0, integrated environmental variables, ground-surface texture information, and the "DB" variable. For comparative analysis, three additional models were constructed: Model 1, a traditional model that incorporated only environmental variables (PRE, DW, LST, NDVI, WET, LU, and NL); Model 2, combining environmental variables with ground-surface texture information; and Model 3, merging environmental variables with the "DB" variable. To ensure consistency in the number of covariates, randomly permuted texture information was added as a control in models lacking texture information (Model 1 and Model 3) [26]. RF models were constructed by Python's Scikit-learn machine learning library [27].

We first excluded the presence of multicollinearity as well as variables with low significance by using Pearson correlation calculation and Recursive Feature Elimination (RFE). The dataset for model validation was partitioned into two segments: 75 % allocated for the training set and 25 % for the validation set [9]. During the dataset division process, we employed the stratified sampling method to ensure that the proportions of positive and negative samples in both the training and test sets matched those of the original dataset. Specifically, we selected 81 positive and 81 negative samples for the training set, and 27 positive and 27 negative samples for the test set.

The training set was utilized to fit the model, allowing it to learn the relationship between the input features and the target variable. To prevent statistical randomness, 10-fold cross-validation and grid search was employed to ascertain optimal parameters and evaluate models using test sets data. The optimal parameters for the model were determined to be min_samples_leaf = 1, min_samples_split = 2, and n_estimators = 200. Where min_samples_leaf represents the minimum of samples per leaf node, min_samples_split represents the minimum of samples required to split an internal node, and the n_estimators represents the total of trees in the forest. Model performance was assessed using four indicators: true skill statistic (TSS) [28], accuracy (ACC) [29], kappa [30], and area under the curve (AUC) [31]. These indicators facilitated a comparative analysis of model performances [9,32]. Subsequently, the best-performing model was applied to predict snail habitat probabilities across the study area. Finally, we created grids with size of 30 × 30 m, extracted data on the natural environment within the study area by grids, predicted snail habitat probabilities using a trained RF model, and then interpolated the probabilities into maps to generate snail habitat maps.

3 Results

3.1 Risk probability map of snail habitats

Fig. 3 presents the risk probability maps used in this study. It details the baseline model's identification of high-risk snail habitats (probability of snails' presence being more than 0.7), predominantly along the Yangtze River and Qiupu River in the northern and central parts of the study area, near Shengjin Lake in the south, and around the Jiuhua River and Qingtong River in the eastern part of the study area. Conversely, low-risk areas (probability below 0.3) include the northeastern residential zones, the western bank of the Qiupu River, and remote agricultural lands. Notably, three alternative models identified more extensive high-risk areas compared with the baseline model. Specifically, Model 1 and Model 3 suggest a greater risk of snail habitat in the western part of the study area compared to the eastern part, whereas the high-risk areas identified in Model 2 were mainly concentrated in the waterbody regions.Fig. 3 Probability map showing the distribution of snail habitats. a: Model 0, characterized by texture information of ground-surface, DB, and environmental variables; b: Model 1, characterized by environmental variables; c: Model 2, characterized by texture information and environmental variables; d: Model 3, characterized by DB and environmental variables.

Fig. 3

3.2 Model comparison

Table 3 presents a comparative analysis of model performances. The baseline model emerged as the most effective, registering the highest scores across all indicators (TSS = 0.93, ACC = 0.97, kappa = 0.94, AUC = 0.99). It was closely followed by Model 2 (TSS = 0.91, ACC = 0.95, kappa = 0.86, AUC = 0.96) and Model 3 (TSS = 0.88, ACC = 0.94, kappa = 0.89, AUC = 0.96). By contrast, Model 1 exhibited comparatively weaker performance (TSS = 0.83, ACC = 0.91, kappa = 0.75, AUC = 0.85).Table 3 Type and comparison of models.

Table 3Model	Model 0	Model 1	Model 2	Model 3	
Whether Environmental Variables is Included	Yes	Yes	Yes	Yes	
Whether Texture Information is Included	Yes	No	Yes	No	
Whether "DB" variable is Included	Yes	No	No	Yes	
TSS	0.926	0.825	0.907	0.883	
ACC	0.969	0.912	0.954	0.944	
Kappa	0.937	0.752	0.863	0.887	
AUC	0.993	0.846	0.957	0.962	
Note: TSS, true skill statistic; ACC, accuracy; AUC, area under the curve.

3.3 Importance of variables

Through correlation assessment and feature selection, we excluded five variables: B5, B7, B2, B4, and LU. The heatmap of variable correlation coefficients is shown in Appendix Figure S8. Fig. 4 illustrates the variable importance rankings in the baseline model. The vertical axis represents variable categories, while the horizontal axis signifies the importance value (IV) of the variables. Out of 10 covariates, four—comprising three environmental and one texture variables—had an importance value exceeding 0.07. The most significant variable was DB (IV = 0.45), markedly outperforming others in importance. This was followed by B6 (IV = 0.14), NL (IV = 0.07), NDVI (IV = 0.07) and LST (IV = 0.07). The variables WET, PRE, B3, B1, B8 had importance scores below 0.07.Fig. 4 Variable importance ranking of the baseline model (Model 0). The vertical coordinate indicates the category of the variable and the horizontal coordinate indicates the importance of variables. DB, distance to bottomland; B6, entropy; NL, nighttime light; NDVI, normalized difference vegetation index; LST, land surface temperature; WET, wetness; PRE, precipitation; B3, standard deviation; B1, mean; B8, correlation.

Fig. 4

4 Discussion

This study aimed to identify high-risk zones of snail habitats using remote sensing imagery. Therefore, a model for identifying snail habitats was developed, which integrates traditional environmental variables with ground-surface texture information and the DB variable. The comprehensive approach effectively pinpointed high-risk sites for snail habitats in the study area. The generated probability map of snail habitat serves as a tool for assisting relevant personnel in precisely focusing on snail control and schistosomiasis prevention education, thereby improving the effectiveness of snail detection and eradication efforts.

Table 3 indicates that, among the four models, the baseline model, incorporating ground-surface texture information and the variable "DB", outperforms others in predictive accuracy. Conversely, Model 1, reliant solely on environmental factors, exhibits the lowest accuracy. This discrepancy underscores the substantial contributions of the DB variable and ground-surface texture information in enhancing model performance. The superior performance of the baseline model can be attributed to its comprehensive evaluation of spatial factors associated with snail habitats and its consideration of seasonal aspects of reproduction.

The analysis of variable importance in the optimal model (i.e., the baseline model) reveals that DB is the most critical predictor, with a relative importance score significantly surpassing other variables. This finding aligns with the results of Li et al. [33], confirming the significant correlation between snail habitat and bottomland distribution. The texture indicators, particularly B6, have proven to be highly influential, surpassing the importance of previously recognized variables such as NL, NDVI, LST and others. The higher B6 value indicates that the image has higher complexity and rich color variation. Snail habitats are mostly located in areas such as overgrown mudflat, where the texture information of ground surface is very rich, which is different from the features in no snail areas such as year-round lakes, rooftops, and roads, where the surface information is simple and smooth [34]. In addition, in the baseline model, three variables, NL, NDVI, and LST, were almost equally important, followed by WET and PRE, of which nighttime light data showed a higher level of importance. Nighttime light is closely related to economic conditions, which can reflect urbanization, population and industry, while economic development and human activities have been shown to negatively affect snail habitats [35]. In addition, many studies have shown that NDVI, LST, WET and RPE are important factors influencing the snail habitats, which our study was also confirmed [[34], [35], [36]]. Suitable vegetation and climatic conditions provide nutrients and ideal environment for snail breeding, while rainfall and wetness variables provide essential moisture for snail growth [37]. The three texture indicators, B1, B3, and B8, helped the model to identify texture patterns and feature distribution in the study area, facilitating the identification of potential features associated with snail habitats. The dominant snail habitat community, Cyperaceous, exhibits specific patterns or structures at the textural scale, and incorporating textural information helps capture these differences. Additionally, texture information aids in understanding the spatial structure and distribution of features, such as the belt-like distribution of vegetation on bottomland along the Yangtze River [38,39], thus improving the model's accuracy in identifying the distribution of vegetation and snail habitats. With the combination of texture information and environmental variables, there is a significant improvement in the model performance.

Fig. 3 depicts risk probability maps generated by the baseline model and three other models. These maps identify key high-risk areas (warm-color areas with risk probability >0.7) for snail habitats along major rivers and lakes, contrasting with low-risk areas in residential and agricultural regions. The high-risk area shown in Fig. 3b (Model 1) is large and concentrated in the western part of the study area, with less risk in the eastern part. In contrast, Fig. 3c (Model 2) further refines the distribution of high-risk areas, the introduction of surface texture information in Model 2 made it easier for the model to capture information on vegetation, features, and spatial structures favorable for snail habitats. However, the extent of high-risk areas in Fig. 3c is still larger compared to the baseline model, and the differences between the two models were mainly located in the portions of perennially inundated rivers and lakes, such as the northernmost part of the study area in the Yangtze River basin and the eastern part of the study area, which includes almost the entire Qingtong River basin. This discrepancy is likely due to the fact that Model 2 ignores the seasonality of snail reproduction and identifies perennially flooded areas as suitable for snail survival. The introduction of the DB variable ameliorated this issue. Fig. 3d (Model 3) exhibits a similar problem with Fig. 3b, as both models classify the Yangtze River coast in the northeastern part of the study area as a low-risk area (cold-color areas with risk probability <0.3). However, the inclusion of bottomland area information helps the model to identify high-risk area near the watershed, especially in the east part of the study area, and the model better distinguishes between high-risk areas along the Yangtze River and the Qiupu River, and low-risk zones south of the Yangtze River.

This detailed mapping enables public health authorities, including the local Centers for Disease Control and Prevention, to focus on snail detection and eradication efforts, customizing strategies for physical extermination and drug delivery in areas with higher snail probabilities. Additionally, the maps support targeted educational initiatives in high-risk regions, raising awareness about schistosomiasis prevention and control. The methodology applied in this study shows promising for broader application in similar contexts. Guichi was chosen as the study area due to its typical snail habitats in lake and marshland environments, and its conditions favor snail survival, making it a critical area for Schistosomiasis japonica in China [14,[40], [41], [42]]. Our study is not only regionally significant but also broadly representative. The developed model can serve as a reference for snail habitats in lake and marshland areas with similar environmental conditions. Additionally, this study's approach can assist in locating breeding sites for intermediate hosts of other diseases, such as Zika, dengue fever, and malaria, which are heavily influenced by environmental factors [[43], [44], [45]]. The approach used in this study offers a comprehensive understanding of the relationship between these hosts and their environments.

This study acknowledges certain constraints requiring additional discussion. First, our study primarily targeted snail habitats in lake and marshland areas, potentially limiting its applicability in hilly and mountainous regions where environmental conditions vary significantly. These areas feature diverse elevation, slope gradients, and soil compositions, which influence moisture retention and vegetation—key factors for snail survival. To enhance the model's predictive accuracy across such varied landscapes, it is essential to develop tailored models that incorporate these specific ecological conditions, which will improve the precision of snail distribution predictions and support the effective targeting of disease control measures in these complex ecosystems. Second, this study did not incorporate the effects of snail control strategies such as pharmacological disinfestation. Such measures can dramatically alter snail population dynamics and, in turn, affect the transmission of schistosomiasis. The omission of these factors may have led to an oversimplified interpretation of snail habitat suitability. Unfortunately, data on snail control strategies have not been available. Future research will aim to collect comprehensive data on the efficacy of various snail control methods and their ecological impacts. Incorporating these variables will enhance our model, making it a more robust tool for predicting snail distributions and supporting the targeted application of control measures. Third, since the data on snail distribution were concentrated in March to May of 2021, we did not account for seasonal variations of other environmental factors except for the "DB" variable, but calculated the mean values of the factors that play a major role in snail distribution throughout the year based on the characteristics of the snail habitats, and some of the ecological factors that show seasonal characteristics that we did not include in the model (Temperature seasonality, Maximum air temperature in the warmest month, Temperature annual range, etc.), which may lead to the neglect of seasonal factors related to snail distribution.

5 Conclusion

This study reveals that high-risk areas for snail distribution in Guichi, China, are predominantly found along rivers, particularly near the Yangtze River, Qiupu River, and surrounding areas of Shengjin Lake, Jiuhua River, and Qingtong River. Our model, upon validation, outperformed traditional models in identifying high-risk areas. The inclusion of textural information and bottomland variables significantly enhanced the model performance, resulting in more accurate identification of snail habitats. Our comprehensive analysis highlighted the significant influence of textural information, natural environment, climate, and social factors on the distribution of snail habitats. It also revealed the close relationship between seasonal water level changes and snail breeding. However, the model was only applicable to predicting snail populations in lakes and marshlands and did not consider snail control measures. We plan to collect relevant data for further study in the future. These findings are critical for guiding focused interventions, such as targeted snail control measures, preventive education on schistosomiasis, and related activities. Furthermore, the methodology of this study has potential for broader application in identifying breeding sites for other disease vectors influenced by environmental factors, thereby enhancing the efficiency and precision of disease prevention and control efforts.

Data availability

All the environmental data is available in a public repository at https://figshare.com/articles/dataset/environmental_data_of_snail_habitat_identification/25092638, and data on snail habitat can be available on request from the corresponding author.

CRediT authorship contribution statement

Xuedong Zhang: Writing – original draft, Validation, Funding acquisition. Zelan Lv: Writing – review & editing, Writing – original draft, Software, Methodology, Formal analysis, Data curation. Jianjun Dai: Resources, Project administration, Investigation. Yongwen Ke: Validation, Project administration, Investigation. Xinyue Chen: Validation, Data curation. Yi Hu: Writing – review & editing, Validation, Supervision, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgment

This work is primarily being funded by Beijing Key Laboratory of Urban Spatial Information Engineering (NO. 20230101 ) and 10.13039/501100001809 National Natural Science Foundation of China (NO. 81773487 ).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36300.
==== Refs
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